Generative Adversarial Networks (GANs) have been widely popularized in diverse image synthesis tasks, such as image generation [1]}, [2]}, image translation [3]}, [4]} and super resolution [5]}, [6]}.
However, the ultra-high computational and memory overhead of GANs hinder its deployment on resource-constrained edge devices.
To alleviate this issue, a series of traditional model compression algorithms, i.e., network pruning [7]}, [8]}, weight quantization [9]}, [10]}, low-rank decomposition [11]}, [12]}, knowledge distillation [13]}, [14]}, and efficient architecture design [15]}, [16]} have been applied to reduce calculational consumptions of GANs.
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